Data sourced from Dakota Marketplace, the global LP and GP intelligence platform trusted by thousands of investment professionals. Learn More | Book a Demo
AI tools like Claude and ChatGPT can now connect directly to databases… and for anyone working in private markets, that's a major unlock.
But only if you're actually using it.
The professionals pulling ahead right now aren't waiting for AI to become part of their firm's official process. They're building it into their daily workflow today whether for meeting prep, prospect research, outreach, or competitive intelligence.
The difference between a generic AI tool and the Claude App connected to Dakota Marketplace is the difference between a guess and a grounded answer.
Generic AI has no access to 30 years of verified LP, GP, fund, and transaction data. It hallucinates. It generalizes.
Dakota Marketplace’s Claude App doesn't return rows. It returns intelligence, built on the only dataset built exclusively for the private markets community.
Here's what that looks like in practice, five things we learned today.
1. The Emerging Manager LP Universe
For: Heads of Capital Formation at first-time or emerging PE funds building a prioritized institutional LP target list for a Fund I raise. The Job: Identifying U.S.-based institutional LPs with a documented history of committing to first-time and emerging managers, filtered by commitment size range, PE allocation percentage, and emerging manager program activity.
The prompt
Using Dakota Marketplace, build a prioritized target list of institutional LPs that have a documented history of committing to first-time or emerging PE fund managers — specifically LPs that have made commitments of $10M to $50M to managers on their first or second institutional fund. For each LP, include: institution name, contact name and email for the relevant decision-maker, total AUM, current PE allocation percentage, commitment size range, geographic preference, and any notes on their emerging manager program or first-time manager policy. Filter for U.S.-based LPs only. Exclude any institution already in active dialogue with my firm. Sort by likelihood to commit to an emerging manager based on Dakota data signals.
2. The Non-Sponsored Government Services and Defense Tech Deal Pipeline
For: VPs of Origination at growth equity firms building proprietary pipelines in government services and defense technology ahead of M&A events or contract expansion cycles. The Job: Identifying founder-led, non-sponsor-backed government services and defense tech companies with stable management teams and proximity to major defense contractor hubs that are ready for first institutional capital.
The prompt
Using Dakota Marketplace's company database, identify privately held, non-sponsor-backed companies in the government services and defense technology sectors that have been operating for at least 5 years and show signs of readiness for first institutional capital — specifically a professional management team with a CEO and CFO each in their roles for 2+ years, and headquarters in proximity to D.C., Virginia, or another major defense contractor hub. For each company, show: CEO and CFO names and contact info, estimated revenue tier if available, any prior financing activity, and years since founding.
These prompts are only as good as the data behind them. Every prompt above runs on Dakota Marketplace data: the verified contacts, AUM, investment preferences, and transaction activity that turn a generic AI answer into a real prospect list. Whichever AI app you use, the facts come from the same place. Book a demo of Dakota Marketplace to get connected.
3. The PE Portfolio Company CFO Candidate Pipeline
For: Managing Directors at PE-focused executive search firms running CFO searches at healthcare services and business services portfolio companies. The Job: Building a ranked candidate list of CFOs and VP Finance executives at comparable PE-backed companies, flagged by recent exit completions and tenure signals indicating openness to a new opportunity.
The prompt
I'm running a CFO search for a PE-backed healthcare services company with $180M in revenue. Using Dakota Marketplace, search for executives with CFO or VP Finance titles at PE-backed healthcare or business services companies in the $100M to $500M revenue range. For each candidate, show their current company, title, company revenue or AUM if available, the PE sponsor backing the company, and their professional contact information. Flag any executives who have recently completed a successful exit or have been at their current company for 3 or more years. Return a ranked list of 30 candidates with full contact details I can use to begin outreach.
4. The Scaling Fund Admin Sales Prospect List
For: VPs of Sales at alternative investment fund administration platforms targeting PE and VC managers outgrowing legacy infrastructure. The Job: Identifying registered investment advisers that have grown AUM by 50% or more, now manage three or more active fund vehicles, and remain lean enough to evaluate and switch fund administration providers.
The prompt
Using Dakota Marketplace and Form ADV filings, identify registered investment advisers managing private equity or venture capital funds that have grown their AUM by 50% or more in the last three years, now manage three or more active fund vehicles, and have fewer than 50 employees. For each prospect, provide: firm name, current AUM, number of active funds under management, employee headcount, primary strategy, headquarters city, and key operations or COO contact name and direct email if available.
5. The Infrastructure Debt Manager Landscape Review
For: Directors of Real Assets at corporate pension plans building a competitive manager landscape ahead of issuing an infrastructure debt RFP. The Job: Mapping currently fundraising and recently closed infrastructure debt and infrastructure credit vehicles across established platforms and credible emerging managers, with AUM growth trends and key principal contacts for each.
The prompt
Using Dakota Marketplace, build a comprehensive list of fund managers currently raising or having recently closed an infrastructure debt or infrastructure credit vehicle. For each manager, show: fund name and strategy description — senior vs. subordinated, sector focus, geographic mandate — target fund size and amount raised to date, vintage year of their most recent prior closed fund, key principals with titles and direct contact info, and AUM growth trend from Form ADV filings over the last three years. Our investment committee has approved a new $250M infrastructure debt allocation, and I need to present a complete competitive landscape of 8 to 12 qualified managers before we issue an RFP — covering both established platforms and credible emerging managers in the space.
Start Prompting With Real Data
Here's the thing that makes these prompts work… on its own, AI is brilliant at structure and terrible at facts it doesn't have. Ask any chatbot for a pension fund's current allocation, a CIO's contact, or who actually owns a target company, and it will confidently make something up.
That's the whole reason these prompts run on Dakota Marketplace data, no matter which AI app you prefer: you get the speed and structure of AI with contacts, AUM, allocations, and transactions that are actually verified.
AI is the engine. Dakota Marketplace is the fuel.
Connect the two, in Claude, ChatGPT, or whatever you already use, and the work that used to eat your morning takes minutes, with data you can actually act on.
Book a demo of Dakota Marketplace to get started.
